Various conventional methods and systems exist for determining road surface condition (RSC). Most of these methods and systems rely on analyzing a downward looking camera image, providing information about the road surface directly under a vehicle. Some of these methods and systems rely on focusing a circular light ahead of a vehicle and analyzing a corresponding forward looking camera image, measuring intensity based features such as mean, variance, and maximum pixel intensity within a region of interest (ROI) and implementing probabilistic binary classifiers in order to classify dry, wet, snow, and ice RSCs, optionally followed by a fusion process to generate control parameters for braking, steering, and acceleration sensors. Other of these methods and systems utilize forward looking camera images, transform them into spatial frequency spectra, and analyze the associated frequency distributions corresponding to different RSCs. Still other of these methods and systems utilize forward looking camera images and apply polarization filters and wavelet transformations to classify different RSCs. None of these methods and systems, however, accurately go beyond RSC to generate and use a RFE. This is a fundamental distinction.
RFE estimates the friction level between a road and the tires of a vehicle, across and along the road but especially along the wheel tracks against the surface of the road. Various conventional methods and systems exist for estimating and computing a RFE. However, most of these methods and systems rely on the use of one-dimensional (1-D) on-vehicle sensors, applying physics-based models. These methods generally result in low availability for RFE (typically about 3-5%), resulting in unacceptably low confidence most of the time. In such physics-based models, high confidence occurs only when very significant sliding occurs between the tires and the road. Thus, real time RFEs are sporadic and predictive ability is hampered, diminishing the value of the resulting driver information, delaying and/or limiting automatic control the associated vehicle's motion, and/or falling short of the practical requirements of most cloud-based alert services. This compromises driver safety.
Thus, there is a need for state-of-the-art methods and systems for generating and utilizing a RFE based on forward looking camera images and signal processing for improved accuracy, high availability, and enhanced predictive ability for drivable environmental conditions, while operating efficiently.